{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cache Dataset Tutorial and Speed Test\n",
    "\n",
    "This tutorial shows how to accelerate PyTorch medical DL program based on MONAI CacheDataset.  \n",
    "It's modified from the Spleen 3D segmentation tutorial notebook.\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/MONAI/blob/master/examples/notebooks/cache_dataset_speed.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU \"monai[gdown, nibabel]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Copyright 2020 MONAI Consortium\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "\n",
    "import glob\n",
    "import os\n",
    "import shutil\n",
    "import tempfile\n",
    "import time\n",
    "\n",
    "import IPython\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, Dataset, list_data_collate\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.losses import DiceLoss\n",
    "from monai.metrics import compute_meandice\n",
    "from monai.networks.layers import Norm\n",
    "from monai.networks.nets import UNet\n",
    "from monai.transforms import (\n",
    "    AddChanneld,\n",
    "    Compose,\n",
    "    CropForegroundd,\n",
    "    LoadNiftid,\n",
    "    Orientationd,\n",
    "    RandCropByPosNegLabeld,\n",
    "    ScaleIntensityRanged,\n",
    "    Spacingd,\n",
    "    ToTensord,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.  \n",
    "The dataset comes from http://medicaldecathlon.com/."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resource = \"https://drive.google.com/uc?id=1jzeNU1EKnK81PyTsrx0ujfNl-t0Jo8uE\"\n",
    "md5 = \"410d4a301da4e5b2f6f86ec3ddba524e\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"Task09_Spleen.tar\")\n",
    "data_dir = os.path.join(root_dir, \"Task09_Spleen\")\n",
    "\n",
    "download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set MSD Spleen dataset path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = sorted(glob.glob(os.path.join(data_dir, \"imagesTr\", \"*.nii.gz\")))\n",
    "train_labels = sorted(glob.glob(os.path.join(data_dir, \"labelsTr\", \"*.nii.gz\")))\n",
    "data_dicts = [\n",
    "    {\"image\": image_name, \"label\": label_name}\n",
    "    for image_name, label_name in zip(train_images, train_labels)\n",
    "]\n",
    "train_files, val_files = data_dicts[:-9], data_dicts[-9:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation\n",
    "\n",
    "Deterministic transforms during training:\n",
    "- LoadNiftid\n",
    "- AddChanneld\n",
    "- Spacingd\n",
    "- Orientationd\n",
    "- ScaleIntensityRanged\n",
    "\n",
    "Non-deterministic transforms:\n",
    "- RandCropByPosNegLabeld\n",
    "- ToTensord\n",
    "\n",
    "All the validation transforms are deterministic.  \n",
    "The results of all the deterministic transforms will be cached to accelerate training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def transformations():\n",
    "    train_transforms = Compose(\n",
    "        [\n",
    "            LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            # randomly crop out patch samples from big image based on pos / neg ratio\n",
    "            # the image centers of negative samples must be in valid image area\n",
    "            RandCropByPosNegLabeld(\n",
    "                keys=[\"image\", \"label\"],\n",
    "                label_key=\"label\",\n",
    "                spatial_size=(96, 96, 96),\n",
    "                pos=1,\n",
    "                neg=1,\n",
    "                num_samples=4,\n",
    "                image_key=\"image\",\n",
    "                image_threshold=0,\n",
    "            ),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    val_transforms = Compose(\n",
    "        [\n",
    "            LoadNiftid(keys=[\"image\", \"label\"]),\n",
    "            AddChanneld(keys=[\"image\", \"label\"]),\n",
    "            Spacingd(\n",
    "                keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\"),\n",
    "            ),\n",
    "            Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "            ScaleIntensityRanged(\n",
    "                keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True,\n",
    "            ),\n",
    "            CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "            ToTensord(keys=[\"image\", \"label\"]),\n",
    "        ]\n",
    "    )\n",
    "    return train_transforms, val_transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def train_process(train_ds, val_ds):\n",
    "    # use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "    # to generate 2 x 4 images for network training\n",
    "    train_loader = torch.utils.data.DataLoader(\n",
    "        train_ds, batch_size=2, shuffle=True, num_workers=4, collate_fn=list_data_collate,\n",
    "    )\n",
    "    val_loader = torch.utils.data.DataLoader(val_ds, batch_size=1, num_workers=4)\n",
    "    device = torch.device(\"cuda:0\")\n",
    "    model = UNet(\n",
    "        dimensions=3,\n",
    "        in_channels=1,\n",
    "        out_channels=2,\n",
    "        channels=(16, 32, 64, 128, 256),\n",
    "        strides=(2, 2, 2, 2),\n",
    "        num_res_units=2,\n",
    "        norm=Norm.BATCH,\n",
    "    ).to(device)\n",
    "    loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "    optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "\n",
    "    epoch_num = 600\n",
    "    val_interval = 1  # do validation for every epoch\n",
    "    best_metric = -1\n",
    "    best_metric_epoch = -1\n",
    "    epoch_loss_values = list()\n",
    "    metric_values = list()\n",
    "    epoch_times = list()\n",
    "    total_start = time.time()\n",
    "    for epoch in range(epoch_num):\n",
    "        epoch_start = time.time()\n",
    "        print(\"-\" * 10)\n",
    "        print(f\"epoch {epoch + 1}/{epoch_num}\")\n",
    "        model.train()\n",
    "        epoch_loss = 0\n",
    "        step = 0\n",
    "        for batch_data in train_loader:\n",
    "            step_start = time.time()\n",
    "            step += 1\n",
    "            inputs, labels = (\n",
    "                batch_data[\"image\"].to(device),\n",
    "                batch_data[\"label\"].to(device),\n",
    "            )\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(inputs)\n",
    "            loss = loss_function(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            epoch_loss += loss.item()\n",
    "            print(\n",
    "                f\"{step}/{len(train_ds) // train_loader.batch_size}, train_loss: {loss.item():.4f}\"\n",
    "                f\" step time: {(time.time() - step_start):.4f}\"\n",
    "            )\n",
    "        epoch_loss /= step\n",
    "        epoch_loss_values.append(epoch_loss)\n",
    "        print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "        if (epoch + 1) % val_interval == 0:\n",
    "            model.eval()\n",
    "            with torch.no_grad():\n",
    "                metric_sum = 0.0\n",
    "                metric_count = 0\n",
    "                for val_data in val_loader:\n",
    "                    val_inputs, val_labels = (\n",
    "                        val_data[\"image\"].to(device),\n",
    "                        val_data[\"label\"].to(device),\n",
    "                    )\n",
    "                    roi_size = (160, 160, 160)\n",
    "                    sw_batch_size = 4\n",
    "                    val_outputs = sliding_window_inference(\n",
    "                        val_inputs, roi_size, sw_batch_size, model\n",
    "                    )\n",
    "                    value = compute_meandice(\n",
    "                        y_pred=val_outputs,\n",
    "                        y=val_labels,\n",
    "                        include_background=False,\n",
    "                        to_onehot_y=True,\n",
    "                        mutually_exclusive=True,\n",
    "                    )\n",
    "                    metric_count += len(value)\n",
    "                    metric_sum += value.sum().item()\n",
    "                metric = metric_sum / metric_count\n",
    "                metric_values.append(metric)\n",
    "                if metric > best_metric:\n",
    "                    best_metric = metric\n",
    "                    best_metric_epoch = epoch + 1\n",
    "                    torch.save(\n",
    "                        model.state_dict(), os.path.join(root_dir, \"best_metric_model.pth\"),\n",
    "                    )\n",
    "                    print(\"saved new best metric model\")\n",
    "                print(\n",
    "                    f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "                    f\" best mean dice: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "                )\n",
    "        print(f\"time consuming of epoch {epoch + 1} is: {(time.time() - epoch_start):.4f}\")\n",
    "        epoch_times.append(time.time() - epoch_start)\n",
    "\n",
    "        IPython.display.clear_output()\n",
    "\n",
    "    print(\n",
    "        f\"train completed, best_metric: {best_metric:.4f} at epoch: {best_metric_epoch}\"\n",
    "        f\" total time: {(time.time() - total_start):.4f}\"\n",
    "    )\n",
    "    return (\n",
    "        epoch_num,\n",
    "        time.time() - total_start,\n",
    "        epoch_loss_values,\n",
    "        metric_values,\n",
    "        epoch_times,\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic training and define regular Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "train_trans, val_trans = transformations()\n",
    "train_ds = Dataset(data=train_files, transform=train_trans)\n",
    "val_ds = Dataset(data=val_files, transform=val_trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train with regular Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "(epoch_num, total_time, epoch_loss_values, metric_values, epoch_times,) = train_process(\n",
    "    train_ds, val_ds\n",
    ")\n",
    "print(f\"total training time of {epoch_num} epochs with regular Dataset: {total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enable deterministic training and define Cache Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "set_determinism(seed=0)\n",
    "train_trans, val_trans = transformations()\n",
    "cache_init_start = time.time()\n",
    "cache_train_ds = CacheDataset(\n",
    "    data=train_files, transform=train_trans, cache_rate=1.0, num_workers=4\n",
    ")\n",
    "cache_val_ds = CacheDataset(data=val_files, transform=val_trans, cache_rate=1.0, num_workers=4)\n",
    "cache_init_time = time.time() - cache_init_start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train with Cache Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(\n",
    "    epoch_num,\n",
    "    cache_total_time,\n",
    "    cache_epoch_loss_values,\n",
    "    cache_metric_values,\n",
    "    cache_epoch_times,\n",
    ") = train_process(cache_train_ds, cache_val_ds)\n",
    "print(f\"total training time of {epoch_num} epochs with CacheDataset: {cache_total_time:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot training loss and validation metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 12))\n",
    "plt.subplot(2, 2, 1)\n",
    "plt.title(\"Regular Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "plt.title(\"Regular Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"red\")\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "plt.title(\"Cache Epoch Average Loss\")\n",
    "x = [i + 1 for i in range(len(epoch_loss_values))]\n",
    "y = epoch_loss_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "plt.title(\"Cache Val Mean Dice\")\n",
    "x = [i + 1 for i in range(len(metric_values))]\n",
    "y = metric_values\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.plot(x, y, color=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot total time and every epoch time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(\"train\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"Total Train Time(600 epochs)\")\n",
    "plt.bar(\"regular\", total_time, 1, label=\"Regular Dataset\", color=\"red\")\n",
    "plt.bar(\n",
    "    \"cache\", cache_init_time + cache_total_time, 1, label=\"Cache Dataset\", color=\"green\",\n",
    ")\n",
    "plt.bar(\"cache\", cache_init_time, 1, label=\"Cache Init\", color=\"orange\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"Epoch Time\")\n",
    "x = [i + 1 for i in range(len(epoch_times))]\n",
    "plt.xlabel(\"epoch\")\n",
    "plt.ylabel(\"secs\")\n",
    "plt.plot(x, epoch_times, label=\"Regular Dataset\", color=\"red\")\n",
    "plt.plot(x, cache_epoch_times, label=\"Cache Dataset\", color=\"green\")\n",
    "plt.grid(alpha=0.4, linestyle=\":\")\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
